epysurv.models.timeseries package¶

Submodules¶

epysurv.models.timeseries.convert_interface module¶

Put a timeseries interface in front of all timepoint algorithms.

class epysurv.models.timeseries.convert_interface.Bayes(years_back: int = 0, window_half_width: int = 6, include_recent_year: bool = True, alpha: float = 0.05)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.bayes.Bayes

class epysurv.models.timeseries.convert_interface.Boda(trend: bool = False, season: bool = False, prior: str = 'iid', alpha: float = 0.05, mc_munu: int = 100, mc_y: int = 10, quantile_method: str = 'MM')[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.boda.Boda

class epysurv.models.timeseries.convert_interface.CDC(years_back: int = 5, window_half_width: int = 1, alpha: float = 0.001)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.cdc.CDC

class epysurv.models.timeseries.convert_interface.Cusum(reference_value: float = 1.04, decision_boundary: float = 2.26, expected_numbers_method: str = 'mean', transform: str = 'standard', negbin_alpha: float = 0.1)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.cusum.Cusum

class epysurv.models.timeseries.convert_interface.EarsC1(alpha: float = 0.001, baseline: int = 7, min_sigma: float = 0)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.ears.EarsC1

class epysurv.models.timeseries.convert_interface.EarsC2(alpha: float = 0.001, baseline: int = 7, min_sigma: float = 0)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.ears.EarsC2

class epysurv.models.timeseries.convert_interface.Farrington(years_back: int = 3, window_half_width: int = 3, reweight: bool = True, alpha: float = 0.01, trend: bool = True, past_period_cutoff: int = 4, min_cases_in_past_periods: int = 5, power_transform: str = '2/3')[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.farrington.Farrington

class epysurv.models.timeseries.convert_interface.FarringtonFlexible(years_back: int = 3, window_half_width: int = 3, reweight: bool = True, weights_threshold: float = 2.58, alpha: float = 0.01, trend: bool = True, trend_threshold: float = 0.05, past_period_cutoff: int = 4, min_cases_in_past_periods: int = 5, power_transform: str = '2/3', past_weeks_not_included: int = 26, threshold_method: str = 'delta')[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.farrington.FarringtonFlexible

class epysurv.models.timeseries.convert_interface.GLRNegativeBinomial(alpha: float = 0, glr_test_threshold: int = 5, m: int = -1, change: str = 'intercept', direction: Union[Tuple[str, str], Tuple[str]] = ('inc', 'dec'), upperbound_statistic: str = 'cases', x_max: float = 10000.0)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.glr.GLRNegativeBinomial

class epysurv.models.timeseries.convert_interface.GLRPoisson(glr_test_threshold: int = 5, m: int = -1, change: str = 'intercept', direction: Union[Tuple[str, str], Tuple[str]] = ('inc', 'dec'), upperbound_statistic: str = 'cases')[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.glr.GLRPoisson

class epysurv.models.timeseries.convert_interface.HMM(n_observations: int = -1, n_hidden_states: int = 2, trend: bool = True, n_harmonics: int = 1, equal_covariate_effects: bool = False)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.hmm.HMM

class epysurv.models.timeseries.convert_interface.OutbreakP(threshold: int = 100, upperbound_statistic: str = 'cases', max_upperbound_cases: int = 100000)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.outbreak_p.OutbreakP

class epysurv.models.timeseries.convert_interface.RKI(years_back: int = 0, window_half_width: int = 6, include_recent_year: bool = True)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.rki.RKI

Module contents¶

class epysurv.models.timeseries.Bayes(years_back: int = 0, window_half_width: int = 6, include_recent_year: bool = True, alpha: float = 0.05)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.bayes.Bayes

class epysurv.models.timeseries.Boda(trend: bool = False, season: bool = False, prior: str = 'iid', alpha: float = 0.05, mc_munu: int = 100, mc_y: int = 10, quantile_method: str = 'MM')[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.boda.Boda

class epysurv.models.timeseries.CDC(years_back: int = 5, window_half_width: int = 1, alpha: float = 0.001)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.cdc.CDC

class epysurv.models.timeseries.Cusum(reference_value: float = 1.04, decision_boundary: float = 2.26, expected_numbers_method: str = 'mean', transform: str = 'standard', negbin_alpha: float = 0.1)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.cusum.Cusum

class epysurv.models.timeseries.EarsC1(alpha: float = 0.001, baseline: int = 7, min_sigma: float = 0)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.ears.EarsC1

class epysurv.models.timeseries.EarsC2(alpha: float = 0.001, baseline: int = 7, min_sigma: float = 0)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.ears.EarsC2

class epysurv.models.timeseries.FarringtonFlexible(years_back: int = 3, window_half_width: int = 3, reweight: bool = True, weights_threshold: float = 2.58, alpha: float = 0.01, trend: bool = True, trend_threshold: float = 0.05, past_period_cutoff: int = 4, min_cases_in_past_periods: int = 5, power_transform: str = '2/3', past_weeks_not_included: int = 26, threshold_method: str = 'delta')[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.farrington.FarringtonFlexible

class epysurv.models.timeseries.Farrington(years_back: int = 3, window_half_width: int = 3, reweight: bool = True, alpha: float = 0.01, trend: bool = True, past_period_cutoff: int = 4, min_cases_in_past_periods: int = 5, power_transform: str = '2/3')[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.farrington.Farrington

class epysurv.models.timeseries.GLRNegativeBinomial(alpha: float = 0, glr_test_threshold: int = 5, m: int = -1, change: str = 'intercept', direction: Union[Tuple[str, str], Tuple[str]] = ('inc', 'dec'), upperbound_statistic: str = 'cases', x_max: float = 10000.0)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.glr.GLRNegativeBinomial

class epysurv.models.timeseries.GLRPoisson(glr_test_threshold: int = 5, m: int = -1, change: str = 'intercept', direction: Union[Tuple[str, str], Tuple[str]] = ('inc', 'dec'), upperbound_statistic: str = 'cases')[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.glr.GLRPoisson

class epysurv.models.timeseries.HMM(n_observations: int = -1, n_hidden_states: int = 2, trend: bool = True, n_harmonics: int = 1, equal_covariate_effects: bool = False)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.hmm.HMM

class epysurv.models.timeseries.OutbreakP(threshold: int = 100, upperbound_statistic: str = 'cases', max_upperbound_cases: int = 100000)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.outbreak_p.OutbreakP

class epysurv.models.timeseries.RKI(years_back: int = 0, window_half_width: int = 6, include_recent_year: bool = True)[source]¶: Bases: epysurv.models.timeseries._base.NonLearningTimeseriesClassificationMixin, epysurv.models.timepoint.rki.RKI